For all experiments, we evaluated the predictions against true labels using five evaluation metrics: Precision (P), Recall (R), F1, Accuracy (Acc) and Matthews Correlation Coefficient (MCC) [1]}. Formulas are provided below in Equations (1) to (5), where \(TP\) / \(TN\) / \(FP\) / \(FN\)  stands for True Positive/True Negative/False Positive/ False Negative respectively. All metrics were calculated using the scikit-learn [2]} package.
\(P = \frac{TP}{TP + FP}\) 
\(R = \frac{TP}{TP + FN}\) 
\(F1 = \frac{2 \times P \times R}{P+R}\) 
\(Acc = \frac{TP + TN}{TP + TN + FP + FN}\) 
\(MCC = \frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}\) 
